Data-driven, cross-sectional image-based subtyping and staging of brain volumetric changes in Parkinson's disease

Several subtyping methods have been proposed to characterize Parkinson's disease (PD) progression, yet the trajectory of subcortical and cortical neurodegeneration and its clinical implications remain unclear.

We aimed to conduct a strictly image-based, data-driven classification of PD progression through Subtype and Stage Inference (SuStaIn) algorithm.

Brain volumetric data from 565 patients with PD and 150 propensity-matched healthy controls were analyzed. 16 regions of interest, including 9 cortical and 7 deep grey matter structures, were segmented from T1-weighted magnetic resonance images. Clinical data, including REM sleep behavior disorder (RBD), levodopa equivalent daily dose (LEDD), and motor complications were collected. SuStaIn was trained and tested using a 10-folds cross-validation and identified two distinct PD progression subtypes, which were compared for differences in clinical and radiological characteristics.

We found two distinct neurodegenerative trajectories: deep grey matter (DG)-first and cortex (CO)-first. The CO-first subtype had a higher prevalence of RBD (p = 0.009) and levodopa-induced dyskinesia (p = 0.024) than the DG-first subtype. Disease progression was faster in the CO-first subtype (0.203 year/stage, LEDD increase 59.3 mg/year), than in the DG-first subtype (0.081 year/stage, LEDD increase 45.1 mg/year, respectively). Regardless of the subtypes, the sensorimotor and auditory cortices were the earliest affected cortical regions, while the amygdala was the first affected subcortically. A subset of participants (n = 186) showed no significant atrophy progression.

Our findings support the existence of two distinct subtypes of PD progression based on neuroimaging data. Longitudinal studies are warranted to track their evolution.